Search Results (66)

Accurate prediction and optimized control of shield tunneling attitude are critical for ensuring tunneling quality and construction safety. In karst and other highly heterogeneous strata, complex geological conditions and construction parameters exhibit significant nonlinear coupling, greatly increasing the difficulty of attitude regulation. To address this challenge, this study proposes a machine learning-based approach for the automatic control of shield tunneling attitude. First, a Tree-structured Parzen Estimator-optimized Light Gradient Boosting Machine predictive model is employed to construct a nonlinear mapping model between construction parameters and shield tunneling attitude. Subsequently, the SHapley Additive exPlanations (SHAP) interpretability model is introduced to identify the core tunneling factors influencing attitude stability. On this basis, the developed predictive model is integrated into the multi-objective evolutionary algorithm based on decomposition (MOEA/D) framework as a fitness function to achieve multi-objective optimization of key construction parameters. Using field data from shield tunneling construction in the karst strata of Shenzhen Metro Line 16, the proposed model achieved prediction accuracies of R² = 0.959 for pitch and R² = 0.936 for roll, outperforming XGBoost, Random Forest, Long Short-Term Memory, and Transformer baselines. SHAP analysis identified the partitioned propulsion thrust, partitioned chamber pressure, cutterhead rotational speed, and advance rate as key parameters influencing attitude. Further, MOEA/D optimization generated a Pareto set of construction parameters, from which the optimal solution was selected using the ideal point method, resulting in reductions of 26.45% and 39.47% in pitch and roll deviations, respectively. These findings demonstrate the feasibility and effectiveness of the proposed method in achieving high-precision prediction and intelligent optimization control of shield tunneling attitude under complex geological conditions, providing a reliable technical pathway for metro and tunnel construction projects. Full article

HiRo-SLAM is a visual-inertial SLAM system developed to achieve high accuracy and enhanced robustness. To address critical limitations of conventional methods, including systematic biases from imperfect camera models, uneven spatial feature distribution, and the impact of outliers, we propose a unified optimization framework that integrates four key innovations. First, Precise Camera Projection Modeling (PCPM) embeds a fully differentiable camera model in nonlinear optimization, ensuring accurate handling of camera intrinsics and distortion to prevent error accumulation. Second, Visibility Pyramid-based Adaptive Non-Maximum Suppression (P-ANMS) quantifies feature point contribution through a multi-scale pyramid, providing uniform visual constraints in weakly textured or repetitive regions. Third, Robust Optimization Using Graduated Non-Convexity (GNC) suppresses outliers through dynamic weighting, preventing convergence to local minima. Finally, the Point-Line Feature Fusion Frontend combines XFeat point features with SOLD2 line features, leveraging multiple geometric primitives to improve perception in challenging environments, such as those with weak textures or repetitive structures. Comprehensive evaluations on the EuRoC MAV, TUM-VI, and OIVIO benchmarks show that HiRo-SLAM outperforms state-of-the-art visual-inertial SLAM methods. On the EuRoC MAV dataset, HiRo-SLAM achieves a 30.0% reduction in absolute trajectory error compared to strong baselines and attains millimeter-level accuracy on specific sequences under controlled conditions. However, while HiRo-SLAM demonstrates state-of-the-art performance in scenarios with moderate texture and minimal motion blur, its effectiveness may be reduced in highly dynamic environments with severe motion blur or extreme lighting conditions. Full article

Thin-walled parts are widely used in the automotive manufacturing industry due to their lightweight characteristics and high structural efficiency. However, it is difficult to accurately measure machining errors in key regions due to the feature deformation. To improve the online measurement accuracy of complex thin-walled parts, a machining error measurement approach based on multi-constraint point cloud registration is proposed. To address the low overlap and complex geometric features among multi-segment measured point clouds, a point cloud stitching method based on hole boundary features is developed to acquire complete measured point clouds. Meanwhile, a point cloud surface extraction method based on normal neighborhood searching is developed to acquire model point clouds. Since different regions of thin-walled parts require different geometric tolerances, a registration model integrating multiple locating and assembly constraints is proposed to satisfy the requirements for optimal point cloud registration. A measurement system composed of a line-structured light sensor and a six-axis robotic arm is developed to validate the proposed method. Experimental results show that the proposed approach reduces the overall dimensional error of point cloud stitching by approximately 70–86% and decreases the point number deviation between upper and lower surfaces by more than 98%. Furthermore, the measurement accuracy in locating holes and key assembly regions is improved to 0.05 mm and 2 mm, representing improvements of approximately 96.3% and 23.9% compared with registration methods without multi-constraint conditions, and approximately 95.3% and 14.5% compared with commonly used multi-constraint registration methods. Full article

The extraction of the light stripe center is a pivotal step in line-structured light vision measurement. This paper addresses a key challenge in the online measurement of train wheel treads, where the diverse and complex profile characteristics of the tread surface lead to uneven gray-level distribution and varying width features in the stripe image, ultimately degrading the accuracy of center extraction. To solve this problem, a region-adaptive multiscale method for light stripe center extraction is proposed. First, potential light stripe regions are identified and enhanced based on the gray-gradient features of the image, enabling precise segmentation. Subsequently, by normalizing the feature responses under Gaussian kernels with different scales, the locally optimal scale parameter (σ) is determined adaptively for each stripe region. Sub-pixel center extraction is then performed using the Hessian matrix corresponding to this optimal σ. Experimental results demonstrate that under on-site conditions featuring uneven wheel surface reflectivity, the proposed method can reliably extract light stripe centers with high stability. It achieves a repeatability of 0.10 mm, with mean measurement errors of 0.12 mm for flange height and 0.10 mm for flange thickness, thereby enhancing both stability and accuracy in industrial measurement environments. The repeatability and reproducibility of the method were further validated through repeated testing of multiple wheels. Full article

To enhance the overall quality and consistency of depth maps, Digital Surface Models (DSM), and True Digital Orthophoto Map (TDOM) in UAV image reconstruction, this paper proposes a multi-stage adaptive optimization generation method. First, to address the noise and outlier issues in depth maps, an adaptive joint bilateral filtering-based optimization method is introduced. This method repairs anomalous depth values using a four-directional filling strategy, incorporates image-guided joint bilateral filtering to enhance edge structure representation, effectively improving the accuracy and continuity of the depth map. Next, during the DSM generation stage, a method based on depth value voting space and elevation anomaly detection is proposed. A joint mechanism of elevation calculation and anomaly point detection is used to suppress noise and errors, while a height value completion strategy significantly enhances the geometric accuracy and integrity of the DSM. Finally, in the TDOM generation process, occlusion detection and gap-line generation techniques are introduced. Together with uniform lighting, color adjustment, and image gap optimization strategies, this improves texture stitching continuity and brightness consistency, effectively reducing artifacts caused by gaps, blurriness, and lighting differences. Experimental results show that the proposed method significantly improves depth map smoothness, DSM geometric accuracy, and TDOM visual consistency compared to traditional methods, providing a complete and efficient technical pathway for high-quality surface reconstruction. Full article

The Space Solar Power Station/Satellite (SSPS) is a large-scale space-borne facility intended for the direct collection and conversion of solar energy in the extra-stratospheric region. The optimization of its light collection and conversion (LCC) structures, analysis of dynamic characteristics, and design of attitude control systems represent core technical bottlenecks impeding the advancement of SSPS. To address these issues, this study investigates a novel conceptual line-focusing SSPS. Firstly, a multi-objective collaborative optimization model is developed to optimize the structural parameters of the concentrator and photovoltaic (PV) array. Subsequently, based on the optimized parameters, a coupled multi-body dynamic model is formulated, incorporating gravity-gradient torque and other space-borne disturbance factors. Finally, a distributed Proportional–Integral–Derivative (PID) controller is proposed to achieve three-axis attitude stabilization of the SSPS. Simulation results demonstrate that the light collection efficiency achieves 81.9% with a power density of 4792.24 W/m²; concurrently, a balance between the geometric parameters of the LCC system and the aforementioned key performance indicators is attained, and the proposed controller possesses favorable anti-disturbance performance. Full article

To address the problem of low detection accuracy caused by uneven brightness distribution in transmission line inspection images under complex lighting conditions, this paper proposes an unsupervised image enhancement method that integrates grayscale feature guidance and luminance consistency loss constraint. First, a U-shaped generator combining a bottleneck structure with large receptive field depthwise separable convolutions is designed to efficiently extract multi-scale features. Second, a grayscale feature-guided image generation module is incorporated into the generator, using grayscale information to adaptively enhance local low-light regions and effectively suppress overexposed regions. Meanwhile, to accommodate the characteristics of unpaired data training, a luminance consistency loss is introduced. By constraining the global luminance distribution consistency between the generated image and the reference image, the overall brightness balance of the generated image is improved. Finally, a multi-level discriminator structure is constructed to enhance the model’s ability to distinguish global and local luminance in the generated images. Experimental results show that the proposed method significantly improves image quality (PSNR increased from 7.73 to 18.41, SSIM increased from 0.43 to 0.85). Furthermore, the enhanced images lead to improvements in defect detection accuracy. Full article

Next-generation photonic memory, leveraging broad spectral operability and electromagnetic immunity, enables ultrafast data storage with high density, overcoming the physical limitations of silicon-based electronic memory in the post-Moore era. Phase-change materials (PCMs) are particularly promising for photonic memory due to their exceptional optical contrast between amorphous and crystalline states. Furthermore, photonic phase-change memory can be deployed as tunable components (such as optical attenuators and delay lines) within reconfigurable integrated photonic systems for telecommunications and computing. Here, we optimize the thickness of PCM cells to maximize crystalline-state light absorption and enhance transmission contrast. The resulting photonic memory achieves outstanding performance: ultralow-energy programming (0.96 pJ/operation), 9 fJ detection sensitivity, >10⁵ s retention, 6000-cycle endurance, and multi-level storage capacity (209 distinct states). Furthermore, by structuring the PCM into a micro-cylinder array atop a PCM film, we achieve stable transmission contrast through 2 × 10⁶ cycles—far exceeding the durability of single-cell structures—and an 8.69 dB improvement in contrast over film-free micro-cylinder arrays. These advances highlight the critical role of microstructural optimization in enabling high-performance, on-chip photonic memory for future integrated photonic telecommunication and computing systems. Full article

Immersion deep ultraviolet (DUV) lithography remains an indispensable core technology in advanced integrated circuit manufacturing, particularly when combined with multiple patterning techniques to achieve sub-10 nm feature patterning. However, at advanced technology nodes, dynamic instabilities of DUV light sources—including spectral characteristics (bandwidth fluctuations, and center wavelength drift), coherence variations, and pulse-to-pulse energy instability—can adversely affect imaging contrast, normalized image log-slope (NILS), and critical dimension (CD) uniformity. To quantitatively assess the impact of laser parameter fluctuations on NILS and CD, this work establishes systematic physical models for imaging perturbations caused by multi-parameter laser output instabilities under immersion DUV lithography. Through simulations, we evaluate the influence of laser parameter variations on the imaging fidelity of representative line/space (L/S) and tip-to-line (T2L) structures, thereby validating the proposed perturbation model. Research demonstrates that the spectral attributes (bandwidth fluctuation and center wavelength drift), coherence variations, and pulse energy instability collectively induce non-uniform electric field intensity distribution within photoresist, degrading NILS, and amplifying CD variation, which ultimately compromise pattern fidelity and chip yield. Notably, at advanced nodes, pulse energy fluctuation exerts a significantly greater influence on imaging errors compared to bandwidth and wavelength variations. To satisfy the 10% process window requirement for 45 nm linewidths, pulse energy fluctuations should be rigorously confined within 1%. This research provides theoretical foundations and practical insights for the design of dynamic stability control of light source and process optimization of next-generation DUV light sources. Full article

Precise online measurement of large structural components is urgently needed in modern manufacturing and intelligent construction, requiring a measurement range over 1 m, near-millimeter accuracy, second-level measurement speed, and adaptability to complex environments. In this paper, three mainstream measurement technologies, namely the image method, line laser scanning method, and structured light method, are comparatively analyzed. The structured light method exhibits remarkable comprehensive advantages in terms of accuracy and speed; however, it suffers from the issue of occlusion during contour measurement. To tackle this problem, multi-camera stitching is employed, wherein the accuracy of camera calibration plays a crucial role in determining the quality of point cloud stitching. Focusing on the cable tightening scenario of meter-diameter cables in cable-stayed bridges, this study develops a contour measurement system based on the collaboration of multiple structured light cameras. Measurement indicators are optimized through modeling analysis, system construction, and performance verification. During verification, four structured light scanners were adopted, and measurements were repeated 11 times for the test workpieces. Experimental results demonstrate that although the current measurement errors have not yet been stably controlled within the millimeter level, this research provides technical exploration and practical experience for high-precision measurement in the field of intelligent construction, thus laying a solid foundation for subsequent accuracy improvement. Full article

Three-dimensional printing technology is fundamentally reshaping the design and fabrication of health monitoring sensors. While it holds great promise for achieving miniaturization, multi-material integration, and personalized customization, the lack of a clear selection framework hinders the optimal matching of printing technologies to specific sensor requirements. This review presents a classification framework based on existing standards and specifically designed to address sensor-related requirements, categorizing 3D printing technologies into point-based, line-based, and area-based modalities according to their fundamental fabrication unit. This framework directly bridges the capabilities of each modality, such as nanoscale resolution, multi-material versatility, and high-throughput production, with the critical demands of modern health monitoring sensors. We systematically demonstrate how this approach guides technology selection: Point-based methods (e.g., stereolithography, inkjet) enable micron-scale features for ultra-sensitive detection; line-based techniques (e.g., Direct Ink Writing, Fused Filament Fabrication) excel in multi-material integration for creating complex functional devices such as sweat-sensing patches; and area-based approaches (e.g., Digital Light Processing) facilitate rapid production of sensor arrays and intricate structures for applications like continuous glucose monitoring. The point–line–area paradigm offers a powerful heuristic for designing and manufacturing next-generation health monitoring sensors. We also discuss strategies to overcome existing challenges, including material biocompatibility and cross-scale manufacturing, through the integration of AI-driven design and stimuli-responsive materials. This framework not only clarifies the current research landscape but also accelerates the development of intelligent, personalized, and sustainable health monitoring systems. Full article

The integration of photovoltaic (PV) panels in greenhouses enables dual land use, combining crop production with electricity generation. However, PV installations can reduce both the intensity and uniformity of light at the canopy level, potentially affecting crop growth. This study employed computational fluid dynamics (CFD) simulations to evaluate the effects of different layouts of commercial-size thin PV modules—both opaque and semi-transparent—installed at gutter height in greenhouses on irradiance and, in particular, on its distribution within the greenhouse. Achieving a homogeneous distribution of light is critical for effective plant growth beneath photovoltaic systems. The influence of greenhouse size and roof shape on the intensity and uniformity of visible radiation was investigated as well. The results showed that during winter (21 December), irradiance in a mono-span tunnel greenhouse was 4–6% higher than in a multi-span large structure; in summer (21 June), this difference increased to 10–13%. Among the opaque PV layouts tested, the north–south (NS) straight-line arrangement provided the most uniform light distribution, outperforming the checkerboard and east–west (EW) layouts. The EW straight-line layout was the least effective regarding light uniformity. Roof shape (arched vs. pitched) had minimal impact on radiation distribution. Semi-transparent PV modules consistently resulted in 17% higher irradiance and more uniform light distribution than opaque ones. These findings can inform efficient PV deployment strategies in greenhouses to enhance both energy yield and crop productivity. Full article

Simultaneous Localization and Mapping (SLAM) is a critical technology for robot intelligence. Compared to cameras, Light Detection and Ranging (LiDAR) sensors achieve higher accuracy and stability in indoor environments. However, LiDAR can only capture the geometric structure of the environment, and LiDAR-based SLAM often fails in scenarios with insufficient geometric features or highly similar structures. Furthermore, low-cost mechanical LiDARs, constrained by sparse point cloud density, are particularly prone to odometry drift along the Z-axis, especially in environments such as tunnels or long corridors. To address the localization issues in such scenarios, we propose a forward-enhanced SLAM algorithm. Utilizing a 16-line LiDAR and a monocular camera, we construct a dense colored point cloud input and apply an efficient multi-modal feature extraction algorithm based on centroid distance to extract a set of feature points with significant geometric and color features. These points are then optimized in the back end based on constraints from points, lines, and planes. We compare our method with several classic SLAM algorithms in terms of feature extraction, localization, and elevation constraint. Experimental results demonstrate that our method achieves high-precision real-time operation and exhibits excellent adaptability to indoor environments with similar structures. Full article

We present the results of Doppler Imaging of the Ap star CU Vir in the silicon lines over the 1985–2011 time span, as well as multi-element imaging in the 2009/2011 epoch. The surface distribution of silicon in CU Vir exhibits stability over the approximately 26 years studied: the number, shape, and mutual distribution of the overabundance spots have remained unchanged. The modelling of the light curve based on the surface elemental distribution obtained with DI did not reveal any significant changes in the shape of the light curve that could explain the photometric phase shift observed in CU Vir. Consequently, the phase shifts and changes in the photometric period of CU Vir are caused by the rigid longitudinal drift of the surface-abundance structures. We performed simulations of the Tayler instability of the background magnetic field of CU Vir, and discuss the possibility of explaining the period variations by the drift of surface instability modes. Full article

Addressing the stability requirements of photonic integrated devices operating over wide temperature ranges, this work achieves controlled fabrication of femtosecond-laser direct-written Type II double-line waveguides and Type III depressed-cladding tubular waveguides within ultra-low-expansion LAS glass-ceramics. The light-guiding mechanisms were elucidated through finite element modeling. The influences of laser writing parameters and waveguide geometric structures on guiding performance were systematically investigated. Experimental results demonstrate that the double-line waveguides exhibit optimal single-mode guiding performance at 30 μm spacing and 120 mW writing power. For the tubular depressed-cladding waveguides, both single-mode and multi-mode fields are attainable across a broad processing parameter window. Large-mode-area characteristics manifested in the 50 μm core waveguide, exhibiting an edge-shifted intensity profile for higher-order modes that generated a hollow beam, enabling applications in atom guidance and particle trapping. Full article